Scaling AI In Industrial Automation: 2026 Data On Workforce Buy-In

Though 72% of manufacturers deploy AI, only 10% scale effectively. Discover how overcoming workforce resistance unlocks enterprise automation growth.

Key Highlights

• Most manufacturers have adopted AI but struggle to scale due to workforce mistrust and communication issues.
• Building AI literacy and involving employees early can improve trust and facilitate broader AI adoption.
• Small, visible wins and continuous performance proof are crucial for earning workforce confidence in AI systems.
• Resistance to AI often stems from uncertainty and lack of clear communication about its impact on jobs.
• Successful AI scaling depends on treating AI literacy as a workforce strategy, not just an IT project.

I have never met a manufacturing leader who would take a new hire, introduce them to new equipment or processes, and then leave them on their own. And yet, that's roughly the shortcut many manufacturers are trying to take with AI. Unlike a new employee who would spend weeks, if not months, slowly accruing responsibility while being shadowed and mentored by a senior team member, many AI rollouts skip the testing and adjusting phases, which are designed to help ensure businesses get the outcomes—and buy-in—they want, and, instead, move straight into production.

As we revealed in our 2026 State of Manufacturing Survey, 72% of surveyed manufacturers have adopted AI in some form—up from 53% just two years ago. Unfortunately, the report also revealed that momentum stalls almost as soon as it starts. Only 10% of those manufacturers have scaled AI and automation across their entire network.

Organizations are getting stuck somewhere between “we're piloting this” and “we trust it and understand what it does for us.” It’s in that in-between place that the returns on AI investments plateau.

Looking at the data, the gap does not appear to be—at least primarily—a technology problem. Manufacturers have proven they can get AI into the building. What they haven't solved is getting their teams to believe in and understand the output they expect to achieve with these new tools.

So, how can business leaders establish that trust and ensure workforce adoption? Visibility, small wins, and time.

Where hesitation lives

Among IT and executive leaders, just 25% describe themselves as genuinely enthusiastic early adopters of AI, while 36% are interested but hesitant. Nearly as many (34%) say they are cautious; they’re waiting to see it proven somewhere else first. In a manufacturing environment, this may translate into a business adopting an AI solution, but failing to achieve its desired outcomes, secure buy-in or complete broader implementation goals.

The barriers leaders report have shifted, too. Infrastructure used to be the primary constraint; now, resistance to change and a shortage of skilled talent rank among the most-cited obstacles to scale. Nearly half of operational leaders cite internal integration skill gaps as a top challenge when bringing on new systems.

Now, there is a stat worth sitting with here. In our 2024 State of Manufacturing Survey, some 30% of respondents cited a lack of skilled talent as a barrier to technology (AI) adoption; two years later, that figure stands at 36%.

Interestingly, move down to the people actually running the equipment, and the picture evolves. More than half (53%) describe themselves as generally receptive to AI. Just 22% say they are resistant to adopting AI, which runs counter to leaders’ perceptions. Perhaps what leaders are characterizing as resistance is something else: uncertainty.

Our data suggests that the roadblock to adoption is really about mistrust. More than half (53%) of manufacturing employees believe AI could replace significant chunks of the workforce. In an industry that is no stranger to automation augmenting—or, in some cases, outright replacing—manual jobs, one can see why even workers who are open to using advanced tools and understand the potential benefits maintain a healthy skepticism about how leaders plan to use them.

That skepticism looks less like resistance and more like a communication failure. Nearly three-quarters (72%) of leaders say upskilling their workforce will be very or extremely important over the next three years. But that intention isn't landing on the floor. Workers who reject AI are reacting to leaders who haven't told them, plainly, what it means for their jobs.

What closes the gap

So, what does this look like in practice? For leaders intent on bolstering adoption and strengthening team morale, treating AI literacy as seriously as any other capital investment becomes non-negotiable. Training must explain the reasoning behind a system's recommendation, not just which button to click. An operator who can see the data behind an alert will trust that alert. One who sees only a red light on a screen has every reason to override or ignore it.

Involving teams in the technology selection process matters just as much. Every manufacturer I've talked with who successfully scaled AI across their organization skipped the company-wide mandate. They started with one team and one specific problem, brought operators in early enough to shape how the tech actually got used, and let small, visible wins do the convincing.

Along those lines, it is paramount that teams have a direct line to whoever owns the AI system. There needs to be a trusted, meaningful system for employees to flag issues, propose solutions and see those solutions not only considered but—when additive to the process—implemented.

Likewise, there is immense value in being upfront about what AI is actually for: making experienced people faster and more precise, surfacing patterns a person might miss and taking repetitive analysis off someone's plate so they can spend their time on the judgment calls that still need a person in the loop. That framing makes an operator's job more valuable, not less.

The manufacturers pulling ahead will treat AI literacy as a workforce strategy, not an IT rollout. That means training employees to understand the reasoning behind AI-driven recommendations, building feedback loops around how operators actually use the technology and ensuring leaders listen to—and act on—what their teams tell them.

Training and trust

On the factory floor, trust is earned through consistent performance. AI will be no different. Like a new hire earning the keys to the line, it will have to prove its value, decision by decision, in front of the people expected to rely on it every day.

The companies that scale AI successfully won’t necessarily be the ones with the most sophisticated algorithms. They’ll be the ones who did the less glamorous work of earning workforce trust and bringing people along with the technology.